EM Algorithm

This document explores the application of Kalman Filtering and Smoothing within the Expectation-Maximization (EM) framework. It provides a comprehensive overview of state estimation, parameter optimization for dynamic models, and the differences between real-time filtering and post-processing smoothing. Key concepts include likelihood versus probability, Gaussian distributions, and the iterative process of maximizing log-likelihood to update model parameters A, C, Q, and R. The interplay between data analysis and algorithmic optimization illustrates how this methodology can improve accuracy in dynamic systems.